De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but often require a high-quality training set that is typically inaccessible in real experimental measurements. Here, using spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we develop a de-noising method for extracting intrinsic spectral information without the need for a training set. This is possible as our method leverages the self-correlation information of the spectra themselves. It preserves the intrinsic energy band features and thus facilitates further analysis and processing. Moreover, since our method is not limited by specific properties of the training set compared to previous ones, it may well be extended to other fields and application scenarios where obtaining high-quality multidimensional training data is challenging.
翻译:在光谱处理后,机学方法在从噪音数据中提取内在信息方面表现良好,但往往需要高质量的培训,在实际实验测量中通常无法获得这种培训。 在这方面,我们以角度解析光源光分光谱学(ARPES)中的光谱为例,开发了一种在不需要培训的情况下提取内在光谱信息的方法。这是可能的,因为我们的方法利用了光谱本身的自我关联信息,它保存了内在能源频带特征,从而便利了进一步分析和处理。 此外,由于与以往相比,我们的方法并不受到成套培训的具体特性的限制,因此它很可能扩大到其他领域和应用情景,而获得高质量的多层面培训数据则具有挑战性。